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ORIGINAL ARTICLE
Effects of the preference for environmental quality
on the export competition between China and
OECD countries
Jung Joo La
Division of Industrial Organization Research, Pi-Touch Institute, Seoul, Korea
KEYWORDS
China’s crowding-out effect, China’s dampening effect, preference for environmental quality
1
|
INTRODUCTION
China’s export growth rate increased rapidly from 6.8% in 2001 to 31.3% in 2010 after it joined
the World Trade Organization (WTO). As a result, there is growing concern that the expansion of
China’s exports may have negative effects on other countries’exports. Studies in this area have
focused mainly on the competition between China and other Asian countries (Eichengreen, Rhee,
& Tong, 2007; Greenaway, Mahabir, & Milner, 2008; Ianchovichina & Walmsley, 2005; Lall &
Albaladejo, 2004; Shafaeddin, 2004). Thus, the question of whether China’s export growth has a
negative effect on the exports of Organisation for Economic Co‐operation and Development
(OECD) countries remains unanswered.
According to Greenaway et al. (2008), China’s export growth is expected to have a negative
effect on high‐income countries’exports to third markets. Greenaway et al. (2008) stressed that
because China’s comparative advantage comes from moving from the production of low‐technol-
ogy, low‐skilled intensive goods to high value‐added and less labour‐intensive manufacturing, its
export growth is having a strong displacement effect on high‐income Asian exporters. However,
this negative effect may not be applicable to intermediate goods. Therefore, a disaggregated, secto-
rial study is needed to reflect the increasing cross‐country complementarity of production processes
that accompany China’s rapid integration into the global production network. According to Eichen-
green et al. (2007), China’s export growth has had a positive effect on the exports of other Asian
countries in third markets for intermediate goods. In accordance with Athukorala (2009), this effect
can be defined as a dampening effect, in that China’s exports grow faster than those of its
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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction
in any medium, provided the original work is properly cited.
© 2018 The Authors. The World Economy Published by John Wiley & Sons Ltd
Received: 18 November 2013
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Revised: 19 August 2018
|
Accepted: 27 August 2018
DOI: 10.1111/twec.12732
World Econ. 2018;1–20. wileyonlinelibrary.com/journal/twec
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1
competitors and retard their growth.
1
Based on prior studies, the crowding‐out or dampening
effects of China’s export growth on the exports of OECD countries are expected.
However, the effects of China’s export growth on those of OECD countries may be weaker
because importers are expected to select exports from OECD countries rather than from China when
they have a strong preference for environmental quality. According to Copeland and Taylor (1994),
higher income countries specialise in producing relatively clean goods. Thus, OECD countries are
expected to export more environmentally friendly goods than China. According to Barboza’s (2007)
report, China’s goods are vulnerable to contamination because of long supply chains with multiple
contractors and subcontractors. This difference in the environmental quality of goods gives rise to the
variation in the preferences for environmental quality across the importing countries. Consequently,
the objective of this study is to analyse how importers’preferences for environmentally friendly prod-
ucts influence the effect that China’s export growth has on the exports of OECD countries.
The gravity model has been used to investigate the effects of China’s exports in a number of prior
studies (e.g., Athukorala, 2009; Eichengreen et al., 2007; Greenaway et al., 2008). However, these
studies do not use a theoretical gravity model to analyse China’s exports; instead, they simply add
exports as a factor into the gravity model. Thus, this study presents a new approach to examining the
effect of China’s export growth on the exports of OECD countries by extending the theoretical gravity
model developed by Anderson and van Wincoop (2003). Specifically, the effect of China’s exports is
incorporated as a component of trade cost into the theoretical gravity equation, which is derived from
general equilibrium analysis. In addition, the unobserved multilateral resistance factors, which are
critical to the model of Anderson and van Wincoop (2003), are examined using the simple first‐order
log‐linear Taylor‐series expansion method proposed by Baier and Bergstrand (2009).
Another key innovation of this study is to ease the constraint of the assumption that importers’
preferences for environmentally friendly products are the same across countries and to propose a
new measure that represents importers’actual preferences for environmental quality across coun-
tries. The existing studies in this area implicitly assume that importers’preferences are the same
and do not consider them to be a significant factor.
The remainder of this paper is organised as follows. Section 2 provides an overview of the
exports of China and selected OECD countries. Section 3 establishes a simple model that serves as
the theoretical framework for the study. Section 4 provides empirical evidence on the influence
that importers’preferences for environmental quality have on the effect of China’s export growth
on the exports of OECD countries based on the new indicator relating to importers’preferences
for environmentally friendly products. Section 5 concludes the paper.
2
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EXPORTS OF CHINA AND OECD COUNTRIES
Figure 1 presents the trends in export growth from 2000 to 2010 for China and OECD countries
to the third markets by sector.
2
China’s exports increased rapidly across all sectors after it joined
the WTO in 2001, whereas OECD countries’exports grew slowly in this period. Thus, China’s
1
In other words, the dampening effect means that the exports of China’s competitors grow below unity due to the rapid
growth of China’s exports. Lall and Albaladejo (2004) regarded this dampening effect as a partial threat.
2
The three sectors are sorted in the HS 96 version according to the classification of Eichengreen et al. (2007) based on
SITC revision 2 as follows: Capital goods fall under the 84, 85(−), 86, 87(−), 88 and 89 codes; consumption goods are
defined as including the 01–24, 30, 61–66, 8527–8528, 8703, 8711–8713, 90–92 and 94–97 codes; and intermediate goods
comprise the 25–29, 31–60, 67–83, 93 and 99 codes.
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export growth appears to have crowded out or dampened that of OECD countries, depending on
the sector. Across the three sectors examined, the most significant gap between China and OECD
countries during the period appears in relation to capital goods, of which China’s exports jumped
sharply from $1,169 million in 2000 to $13,014 million in 2010, whereas those of OECD coun-
tries rose moderately from $1,003 million to $1,425 million on average over the same period.
Hence, it is predicted that the crowding‐out effect of China’s export growth on the exports of
OECD countries, if it exists, is the most severe in this sector.
Table 1 shows the top five export destinations for China and a number of representative OECD
countries based on the average export volumes from 2000 and 2010. China is included in the top five
export markets of almost all of the selected OECD countries for intermediate goods and capital goods,
whereas it is only in the top export market of Japan for consumption goods. OECD countries’exports
to China account for 10.7%, 7.2% and 4.1% of their total exports on average for intermediate goods,
capital goods and consumption goods, respectively, while their average exports to each market
account for 0.4% on average across all sectors, suggesting that China supports the export growth of
OECD countries through its imports from them, especially of intermediate goods and capital goods.
China’s top five export destinations overlap with those of the selected OECD countries across
all sectors, except for South Korea for consumption goods and Germany and the Netherlands for
capital goods, although these three exceptions do belong within the other OECD countries’top 10
export markets.
3
China’s top five export markets account for 55.9%, 45.2% and 56.4% of its total
exports of consumption goods, intermediate goods and capital goods, respectively. OECD
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Unit: Million Dollars
China (Consumption goods)
China (Intermediate goods)
China (Capital goods)
OECD Ave (Consumption goods)
OECD Ave (Intermediate goods)
OECD Ave (Capital goods)
FIGURE 1 Trend of export growth for China and OECD countries to third markets
Note: The value of each year is the average of China and OECD countries across third markets and the gray
lines represent the average levels of 30 OECD countries.
Source: The author’s own estimates based on the UNCOMTRADE data set.
3
South Korea and Germany are included in the top 10 export markets of Australia, Japan and the United States for con-
sumption goods and capital goods, and the Netherlands is a top 10 export market of Germany and Japan for capital goods.
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countries’exports to the corresponding destinations account for 29.4%, 21.7% and 21.6%, on aver-
age, respectively. These figures indicate that there is intense export competition between China
and OECD countries.
Figure 2 shows the trend in the Export Similarity Index (ESI) between China and OECD coun-
tries.
4
First used by Finger and Kreinin (1979), this measure is a good proxy for the level of
export competition between China and OECD countries in third markets. The value for each year
TABLE 1 Top five export destinations of China and selected OECD countries by sector
Rank
China Australia Germany Japan USA
Market Value Market Value Market Value Market Value Market Value
Consumption goods
1 USA 53 Japan 3 USA 31 USA 50 Canada 45
2 Japan 33 USA 3 UK 24 China 8 Mexico 22
3 Hong Kong 26 New
Zealand
2 France 22 Germany 6 Japan 21
4 Germany 9 Saudi
Arabia
1 Italy 22 Australia 5 Germany 13
5 S. Korea 8 UK 1 Belgium 20 Hong
Kong
5UK 10
Intermediate Goods
1 USA 35 Japan 15 France 34 China 33 Canada 72
2 Hong Kong 25 China 14 Netherlands 26 USA 22 Mexico 47
3 Japan 19 S. Korea 7 Italy 24 S. Korea 22 China 19
4 S. Korea 16 India 6 Austria 23 Hong
Kong
11 Japan 17
5 India 7 UK 3 UK 22 Thailand 8 UK 15
Capital Goods
1 Hong Kong 73 USA 1 France 40 USA 57 Canada 87
2 USA 71 New
Zealand
1 USA 30 China 40 Mexico 53
3 Japan 27 Singapore 0.4 UK 24 Hong
Kong
18 Japan 19
4 Germany 18 China 0.4 Italy 19 S. Korea 18 China 18
5 Netherlands 16 Papua New
Guinea
0.3 China 19 Thailand 12 UK 17
Notes: The figures are the average values between 2000 and 2010 and are measured in billions of dollars. The selected OECD
countries represent major regions such as the Oceania, Europe, Asia and North America.
Source: The author’s own estimates based on the UNCOMTRADE data set.
4
The Export Similarity Index is computed as follows:
ESIðcj;iÞ¼ ∑Min½XrðciÞ;XrðjiÞ
fg
100;
where X
r
(ci) is the share of product rin the exports of country cto country iand X
r
(ji) is the share of product r in the
exports of country jto country i.
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represents the average of the ESIs between China and the 30 OECD countries and is calculated
based on the HS 96 version 6‐digit codes. Among the three sectors, the level of export competition
between China and OECD countries is the highest for capital goods and the lowest for consump-
tion goods. In regard to the dynamic pattern of export competition, there is little change for con-
sumption goods during the period of analysis. However, the export competition for capital goods
and intermediate goods is becoming increasingly fierce. These figures are consistent with the
observation that China’s exports are moving from low value‐added to high value‐added goods with
China’s rapidly increasing participation in the global production networks.
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THE MODEL
This study extends the theoretical gravity model developed by Anderson and van Wincoop (2003)
to investigate how importers’preferences for environmentally friendly products influence the effect
of China’s export growth on the exports of OECD countries. The traditional gravity model used in
the literature lacks a theoretical foundation, mainly due to the absence of multilateral resistance
factors, which represent the barriers to trade that each country faces with its trading partners.
Accordingly, Anderson and van Wincoop (2003) develop a new theoretical gravity model to con-
trol for these factors under the assumption that each country specialises in the production of only
one good, which is differentiated by place of origin, and that consumer preferences are identical
and homothetic. The following demand equation is derived from Anderson and van Wincoop’s
(2003) utility maximisation problem:
15
20
25
30
35
40
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Unit: %
Consumption Goods
Intermediate Goods
Capital Goods
FIGURE 2 Trend of Export Similarity Index between China and OECD countries
Note: The value of each year is the average of 30 OECD countries and is calculated on the basis of HS 96
version 6-digit codes.
Source: The author’s own estimates based on the UNCOMTRADE data set.
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Xei ¼yeyi
yw
tei
QePi
1σ
;(1)
where:
Ye¼∑i
tei
Pi
1σ
θi
"#()
1
1σ
;(2)
Pi¼∑e
tei
Qe
1σ
θe
"#()
1
1σ
;(3)
X
ei
is the bilateral trade flow from country eto country i;y
e
(y
i
) is the income of exporting county
e(importing country i); y
w
is the global income; t
ei
is the trade cost between eand i;θ
e
(θ
i
)=y
e
/
y
w
(y
i
/y
w
); and σ= 1/(1−ρ) is the elasticity of substitution among different goods.
Under the assumption that trade costs are symmetric, t
ei
=t
ie
, a solution to Equations (2) and (3)
is Π
e
=P
e
. Then, taking logarithms on both sides of Equation (1), the following equation is derived:
ln Xei ¼αþln yeþln yiðσ1Þln tei þðσ1Þln Peþðσ1Þln Pi;(4)
where P
e
(P
i
) is the CES price index of exporting country e(importing country i), which repre-
sents an unobserved multilateral resistance factor.
Although there are several methods for accounting for unobserved multilateral resistance terms,
the simple first‐order log‐linear Taylor‐series expansion approach proposed by Baier and Berg-
strand (2009) is the most appropriate one for panel data estimation. The custom nonlinear least
squares model introduced by Anderson and van Wincoop (2003) could be computationally chal-
lenging with panel data, and the fixed effect alternative cannot control for time‐varying multilateral
resistance factors (Awokuse & Yin, 2010).
5
Thus, this study follows Baier and Bergstrand (2009)
in representing the multilateral resistance terms as follows:
ln Pe¼∑N
i¼1θiln tei 1
2∑N
k¼1∑N
m¼1θkθmln tkm;(5)
ln Pi¼∑N
e¼1θeln tei 1
2∑N
k¼1∑N
m¼1θkθmln tkm:(6)
Substituting these derived equations into Equation (4) yields:
ln Xei ¼αþln yeþln yiðσ1Þln tei þðσ1Þ∑N
k¼1θkln tek þðσ1Þ∑N
k¼1θkln tki
ðσ1Þ∑N
k¼1∑N
m¼1θkθmln tkm:(7)
In this study, the unobservable trade cost is modelled as the following function of the observ-
able variables:
tei ¼DISβ1
ei eðβ2CTeiþβ3CLei þβ4COei ÞCIeðln
~
Eln EiÞ
i;Ie¼1ifeis an OECD country
0 otherwise;
(8)
where DIS
ei
is the bilateral distance between country eand country i;CT
ei
,CL
ei
, and CO
ei
are
dummy variables indicating whether the countries are contiguous, share a common official language
and have ever had a colonial link, respectively. In addition, C
i
is the exports from China to country
5
Although Baier and Bergstrand (2007) devise country‐and‐time effects as extended fixed effects to account for the time‐
varying multilateral resistance terms, this method leads to overly controlled time‐varying country‐specific variables, includ-
ing those of interest in this study.
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i,I
e
is an indicator function taking the value of one if exporting country eis an OECD country and
zero otherwise, E
i
is a measure of importing country i’s preference for environmentally friendly
exported goods, and
~
Eis the average global level of the preference for environmental quality.
6
Except for CIeðln
~
Eln EiÞ
i, Equation (8) is a log‐linear function of the observable variables derived
from the literature (Anderson & van Wincoop, 2003; Baier & Bergstrand, 2009; Hallak, 2006). The
novelty in specifying the unobservable trade cost relates to the influence of China’s exports to coun-
try i, the intensity of which is determined by the level of importing country i’s preference for envi-
ronmental quality relative to the average global level. If exporting country eis not an OECD
country, then the trade cost function follows that in the literature, which does not consider the varia-
tion in the preference for environmentally friendly goods across importing countries.
As China’s export growth to country ican impede or enhance the exports of OECD countries to
the same markets, as explained, this factor is included in the trade cost function. The level of import-
ing country i’s preference for environmental quality is also added to reflect the influence of the varia-
tion in preferences across importing countries on the export competition between China and OECD
countries. The difference between China and OECD countries in the environmental quality of exports
gives rise to the variation in the preferences for environmental quality across importing countries.
Consequently, an importer with a strong preference for environmental quality is likely to exhibit a
greater demand for exports from OECD countries than for those from China. Therefore, the effect of
China’s export growth on the exports of OECD countries is assumed to be affected by the level of
importing country i’s preference for environmental quality E
i
relative to the world average level
~
E.
Provided that country eis an OECD country, inserting the trade cost from Equation (8) into
Equation (7) generates the following gravity equation:
ln Xei ¼αþln yeþln yiðσ1Þðln
~
Eln EiÞln Ciðσ1Þβ1ln DISei
þðσ1Þβ2CTei þðσ1Þβ3CLei þðσ1Þβ4COei þðσ1Þln
~
EMRCi
ðσ1ÞMRECiþðσ1Þβ1MRDISei ðσ1Þβ2MRCTei
ðσ1Þβ3MRCLei ðσ1Þβ4MRCOei;
(9)
where:
7
MRCi¼∑N
k¼1θkln Ckþ∑N
m¼1θmln Ci∑N
k¼1∑N
m¼1θkθmln Cm;
MRECi¼∑N
k¼1θkln Ekln Ckþ∑N
m¼1θmln Eiln Ci∑N
k¼1∑N
m¼1θkθmln Emln Cm;
MRDISei ¼∑N
k¼1θkln DISek þ∑N
m¼1θmln DISmi ∑N
k¼1∑N
m¼1θkθmln DISkm
hi
;
MRCTei ¼∑N
k¼1θkCTek þ∑N
m¼1θmCTmi ∑N
k¼1∑N
m¼1θkθmCTkm
hi
:
The fourth term on the right‐hand side of Equation (9), ðσ1Þðln
~
Eln EiÞln Ci, shows that the
effect of China’s export growth on OECD countries’exports to third markets is influenced by the
importer’s preference for environmentally friendly products, given that σand
~
Eare constant.
Where environmental preferences are the same across importers, or Ei¼
~
E, Equation (9) follows
6
Environmental quality represents the cleanness of production as defined by Amacher, Koskela, and Ollikainen (2004). The
cleanness of production implies environmentally friendly production and the use of less polluting inputs.
7
MRCL
ei
and MRCO
ei
follow the pattern of MRCT
ei
.
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the gravity model of Anderson and van Wincoop (2003) and the variable for China’s exports lnC
i
disappears. Thus, the practice of simply adding China’s exports to the gravity model as conducted
in prior studies is implausible in this theoretical context.
The influence of importers’preferences for environmental quality on the effect of China’s
export growth on the exports of OECD countries is expected to be valid only in relation to con-
sumption goods and intermediate goods and not capital goods. Consumers may demand more envi-
ronmentally friendly goods to maximise their utility when purchasing consumption goods, given
their increasing preference for environmental quality.
8
Therefore, consumption goods can be
included in the environmental demand sector. Although the users of intermediate goods are firms,
these inputs are closely associated with consumption goods, as they are mostly used to produce
consumption goods. Thus, intermediate goods can be included in the quasi‐environmental demand
sector. The buyers of capital goods take profit maximisation into account by reducing production
costs rather than maximising consumer utility, as they are firms. Hence, capital goods can be trea-
ted as part of the non‐environmental demand sector. Therefore, the argument regarding importers’
preferences for environmental quality is only applicable to the environmental demand and quasi‐
environmental demand sectors, as these sectors are closely related to consumer utility.
4
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EMPIRICAL ANALYSIS
In this section, a measure is first devised to gauge the level of importer preference for environmen-
tal quality across countries for the regression analysis. The econometric methods for estimating the
specifications of the model established in Section 3 are then outlined, the data used described and
the empirical results presented.
4.1
|
Indicator of importer preference for environmental quality
The importer preference for environmental quality indicator (IPEQI) included in the model builds
on an environmental quality indicator (EQI) that ranks export products according to the environ-
mental protection efforts associated with their production. In constructing the EQI, reference is
made to the PRODY index introduced by Hausmann, Hwang, and Rodrik (2007). The PRODY
index measures the weighted average per capita GDP of countries exporting a given product and
thus represents the income level associated with that product. This index has been used by a num-
ber of recent studies as a proxy for the level of export sophistication (e.g., Jarreau & Poncet, 2012;
Minondo, 2010; Xu & Lu, 2009; Yao, 2009). The indicator is calculated as follows:
PRODYn¼∑e
ðxen=XeÞ
∑eðxen=XeÞ
Ye;(10)
where x
en
denotes country e’s exports of product nand X
e
is country e’s total exports. In addition,
ðxen=XeÞ=∑eðxen =XeÞis the revealed comparative advantage of country ein relation to product n
and Y
e
is country e’s per capita GDP based on its purchasing power parity. Hence, PRODYn repre-
sents a weighted average of Y
e
, where the weights correspond to the revealed comparative
8
Arora and Gangopadhyay (1995) reasoned that the growth in firms’voluntary over‐compliance with environmental regula-
tions is due to consumers’preferences for environmental quality. Hamilton and Zilberman (2006) also stressed that con-
sumers’preferences for market goods are as much determined by the environmental attributes of products as by any other
quality attributes. Thus, the demand of consumers with new attitudes towards environmental values may be driving the mar-
ket for green products (Chen, 2001).
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advantage. According to Hausmann et al. (2007), the rationale for using revealed comparative
advantage as a weight is to ensure that the size of a country does not distort the ranking of its
goods. The fundamental assumption underlying the PRODY index is that countries with a higher
per capita GDP export more sophisticated goods. The same logic can be applied in using the EQI
to complement the environmental performance index (EPI), which is a direct and comprehensive
measure of environmental preservation. That is, a country with a higher EPI exports more environ-
mentally friendly goods. The EQI is constructed as follows:
EQIn¼∑e
ðxen=XeÞ
∑eðxen=XeÞ
EPIe:(11)
The EPI is a composite index of the effects of current national environmental protection efforts
devised by the Yale Center for Environmental Law and Policy and the Center for International
Earth Science Information Network at Columbia University, in collaboration with the World Econ-
omic Forum and the Joint Research Center of the European Commission. The EPI has been pub-
lished biannually in a stylised form since 2006. The index builds on measures relevant to two core
objectives: reducing the environmental stress to human health and protecting ecosystems and natu-
ral resources. The second objective is divided into five policy categories: air pollution, water
resources, biodiversity and habitat, productive natural resources, and climate change. The EPI is
aggregated through a weighted average of detailed indicators according to the aforementioned core
objectives and policy categories (e.g., 16 indicators in 2006 and 25 indicators in 2008). The num-
ber of countries covered by the index also varies (e.g., 133 countries in 2006 and 149 countries in
2008). Table 2 reports the average EPI between 2006 and 2008. The value for Sweden, 90.5, is
the highest among the 130 countries, whereas that of Niger, 32.4, is the lowest. The values for
China and OECD countries of interest in this study are 60.6 and 82.6 (average), respectively,
which provides further evidence supporting the argument made in Section 3 that OECD countries
export more environmentally friendly goods than China.
The EQI is now used to construct the IPEQI. The extent of an importer’s preference for envi-
ronmental quality is revealed by the level of imports of green goods. In other words, the IPEQI is
the weighted average of the EQI, where the weights represent the share of each product in the
country’s total imports. The IPEQI is given as follows:
9
IPEQIi¼∑n
min
Mi
EQIn;(12)
where m
in
denotes country i’s imports of product nand M
i
denotes country i’s total imports.
Table 3 shows the empirical results for the IPEQI as constructed by Equation (12). Note that
because the average EQI between 2006 and 2008 is used to construct the IPEQI, the EQI values
used in constructing the IPEQI do not vary over the years.
10
Each statistic is obtained on the basis
9
The EQI is a measure by‐product and the EPI is a measure by country. The latter pays no regard to the sector, such as con-
sumption or intermediate goods. The IPEQI is a measure by country with a component of the EQI. It is a target measure to
gauge the level of importer preference for environmental quality across countries for the regression analysis. Jarreau and
Poncet (2012) used a similar method to measure the sophistication level of imports by country.
10
Current export values need to be transformed into constants for the average EQI, which requires the export price indices
of each country and product. However, because these data are difficult to obtain, the US import price index used by Min-
ondo (2010) is adopted as a good proxy of the average evolution of global export prices. The US import price indices are
obtained from the U.S. Bureau of Labor Statistics. In addition, the weights in Equation (11) are calculated based on the HS
96 version 6‐digit codes, which are the most disaggregated level in terms of international trade data.
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of the average IPEQI from 2000 to 2010. The US export price index is used to transform current
import values into constants. Table 3 reveals that Switzerland and Niger record the maximum
(76.87) and minimum (67.01) values, respectively, among the 99 countries for consumption goods,
and Switzerland and India have the respective maximum (76.14) and minimum (69.38) values for
intermediate goods.
11
As explained by Brecard, Hlaimi, Lucas, Perraudeau, and Salladarre (2009), environmentally
friendly products are more expensive than less environmentally friendly goods, because the former
incur additional costs required to develop environmentally friendly production technologies. More-
over, in this study, green goods are regarded as normal, rather than public goods. Thus, the IPEQI
is expected to be constrained by the importer’s income (Arora & Gangopadhyay, 1995; Brecard et
al., 2009; Franzen, 2003; Torgler & Garcia‐Valinas, 2007). The relationship between income and
the IPEQI is analysed in Table 3 through the correlation coefficient between the logarithm of the
average IPEQI and the logarithm of the average income from 2000 to 2010.
12
The correlation
coefficients between them are positively significant at the 1% level across all sectors. However, the
value for intermediate goods is 0.62, which is unexpectedly low relative to the value of 0.79 for
consumption goods. Trade conducted by multinational corporations provides a possible explanation
for this low value for intermediate goods. According to the study by Helpman (1985), the share of
intra‐firm trade increases as the difference in relative factor endowments widens, provided that the
difference is not too large. This finding supports the argument that subsidiaries in developing
TABLE 2 Summary of environmental performance index (EPI) statistics
No. of observations Mean SD Min. Max.
Statistics 130 68.2 13.2 32.4 (Niger) 90.5 (Sweden)
Selected 60.6 (China) 82.6 (OECD Average)
Note: Each statistic is obtained on the basis of the average EPI between 2006 and 2008.
Source: The author’s own estimates based on the environmental performance index data set.
TABLE 3 Summary of IPEQI statistics
Consumption goods Intermediate goods
Mean 74.25 73.70
SD 1.75 1.35
Min. 67.01 (Niger) 69.38 (India)
Max. 76.87 (Switzerland) 76.14 (Switzerland)
Correl. Coeff. 0.79*** 0.62***
No. of observations 99 99
Notes: Each statistic is obtained on the basis of the average IPEQI from 2000 to 2010. Correl. Coeff. denotes the correlation coeffi-
cient between the logarithm of the average IPEQI and the logarithm of the average income.
***Significance at the 1% level.
Source: The author’s own estimates based on the above EQIs and the UNCOMTRADE data set
11
India is unexpectedly ranked 99th for intermediate goods because it is highly dependent on environmentally poor natural
resources. The items above 5% in terms of the average share of India’s total imports for intermediate goods from 2000 to
2010 are crude oil (30.3%), unwrought gold (9.2%) and unworked diamonds (7.8%).
12
The income is real GDP per capita measured at purchasing power parity in constant 2000 US dollars.
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countries import eco‐friendly intermediate inputs from their parent firms in developed countries to
export finished goods. Therefore, the IPEQIs for developing countries are over‐estimated, which
leads to the low value of the correlation coefficient for intermediate goods.
4.2
|
Econometric method and data description
The specification for estimating Equation (9) using panel data is as follows:
ln Xeit ¼α0þα1ln yet þα2ln yit þα3ln Cit þα4ln Eit ln Cit
þα5ln DISei þα6CTei þα7CLei þα8COei þMRCit
þMRECit þMRDISeit þMRCTeit þMRCLeit þMRCOeit
þϕeþϕiþϕtþɛeit ;
(13)
where ϕ
e
and ϕ
i
represent exporter and importer effects, respectively, which control for all time‐
invariant country characteristics, and ϕ
t
denotes time effects, which account for omitted variables
that are common to all trade flows but vary over time.
As noted in the literature, the estimated parameters α
1
,α
2
and α
6
–α
8
are expected to have a posi-
tive sign such that the relevant variables act as trade‐stimulating factors, whereas the parameter α
5
is
likely to have a negative sign, as distance is a proxy for transportation cost. The estimated parame-
ters α
3
and α
4
of interest in this study are expected to have negative and positive signs, respectively,
in that the negative effect of China’s export growth on the exports of OECD countries is likely to be
weaker in export destinations with a greater preference for environmental quality, where the demand
for exports from OECD countries will be greater than that for goods from China. In accordance with
Eichengreen et al. (2007), the effect of China’s export growth on the exports of OECD countries is
expected to be manifested as a crowding‐out effect in markets for consumption goods, whereas a
dampening effect is observed in markets for intermediate goods.
13
Thus, the slope of the curve repre-
senting China’s exports to country i,α
3
+α
4
ln E
it
is likely to have a negative value for consump-
tion goods and a positive value between 0 and 1 for intermediate goods.
Various econometric methods, such as the ordinary least squares (OLS) method, fixed effects,
random effects and Hausman–Taylor analyses, can be used to estimate Equation (13). However,
OLS estimation may be biased, as it cannot control for the fixed effects of ϕ
e
,ϕ
i
and ϕ
t
. Fixed
and random effect analyses could provide suitable alternatives to control for such factors. How-
ever, fixed effect analysis is more appropriate than random effect analysis, as these factors are
correlated with the explanatory variables in Equation (13). Nevertheless, fixed effect analysis
cannot provide estimates for time‐invariant variables. The Hausman–Taylor analysis can yield
coefficients on time‐invariant variables. The Chinese export variable lnC
it
may be correlated with
the error term, thus causing an endogeneity problem, unless Equation (13) controls for the
effects of other countries’export expansion on the export growth of OECD countries. In addi-
tion, the exporter and importer’s income variables and the multilateral resistance terms, which
have income‐related components, can cause simultaneous bias, as pointed out by Anderson
(1979). The Hausman–Taylor estimation can alleviate endogeneity problems by adopting the
appropriate instrumental variables from within the model. Moreover, the Hausman test of over‐
identification ensures the validity of the instrumental variables. Thus, the null hypothesis of the
13
Although capital goods are not associated with importers’preferences for environmental quality, based on the study by
Greenaway et al. (2008) and the observations presented in Section 2, China’s export growth is expected to have a crowd-
ing‐out effect on the export growth of OECD countries in this sector.
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Hausman test based on a comparison of the fixed effect and Hausman–Taylor estimators should
not be rejected. Therefore, this study adopts the Hausman–Taylor analysis as an econometric
method.
The data used for the regression analyses cover 30 OECD exporting countries and 60 OECD
importing countries over the 2000–10 period.
14
Importing countries that are not consistent across
the variables are excluded from further analysis. In addition, the period in which the export pat-
tern is investigated follows China’s accession to the WTO and an appropriate time span is con-
sidered for the EQI, which is used as a time‐invariant indicator in constructing the IPEQI.
Export data are obtained from the UNCOMTRADE data set and the data on real GDP (mea-
sured on a purchasing power parity basis), population and the strictness of environmental regula-
tions from the World Development Indicator data set. Export values are deflated to 2000
constant US dollars using the US import price indices obtained from the U.S. Bureau of Labor
Statistics. Moreover, the logarithm of one plus the export value is taken for the zero export
value. As these values only occupy 0.27% for consumption goods, 0.29% for intermediate goods
and 0.60% for capital goods in the sample, the manipulation does not need a censoring model
for estimation.
The distance data, which are calculated using the latitudes and longitudes of the most
important cities or agglomerations in terms of population, are sourced from the gravity data set
of the CEPII, a French research centre. Table 4 summarises the variables used in the regression
analyses.
4.3
|
Regression results
Table 5 shows the regression results for Equation (13) by sector obtained using the Hausman–
Taylor estimation. The coefficients of lnCs are significantly negative at the 1% level for con-
sumption goods and intermediate goods, whereas those of the interaction terms between lnC
and lnIPEQI are significantly positive at the same level. For a specific explanation of these
variables for consumption goods, the slope of the lnCcurve is −0.102, −0.092, −0.090,
−0.089 and −0.086 at the minimum (4.254), 1st quartile (4.312), 2nd quartile (4.323), 3rd
quartile (4.329) and 4th quartile (4.348) of the lnIPEQI, respectively. With regard to the expla-
nation for intermediate goods, the slope of the lnCcurve is 0.209, 0.232, 0.237, 0.241 and
0.247 at the minimum (4.229), 1st quartile (4.294), 2nd quartile (4.309), 3rd quartile (4.320)
and 4th quartile (4.337) of the lnIPEQI, respectively. These results suggest that the crowding‐
out effect of China’s export growth observed in markets for consumption goods and the damp-
ening effect for intermediate goods on the export growth of OECD countries are weaker in
export destinations with a greater preference for environmental quality.
15
These results are con-
sistent with this study’s predictions.
The coefficients on the distance variables are significantly negative at the 1% level across sec-
tors and those on the dummy variables regarding common official language and colonial links are
significantly positive at the 1%–5% levels, as expected. Concerning the dummy variable on conti-
guity, there is no robust evidence to support the prediction across sectors. The over‐identification
tests conducted for the Hausman–Taylor estimation do not reject the null hypothesis across sectors,
thus validating the instrumental variables.
14
See Appendix A for the list of countries used in the analysis. Chile, Estonia, Israel and Slovenia are excluded from the list
of OECD countries, as they joined the group in 2010.
15
See Appendix B for the regression results of capital goods.
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TABLE 4 Summary of variables
Variable lnX
eit
lnGDP
et
lnGDP
it
lnC
it
lnC*lnIPEQI
it
lnDIS
ei
lnPOP
et
lnPOP
it
lnSER
et
lnSER
it
Con. goods
No. of observations 19,470 19,470 19,470 19,470 19,470 1,770 19,470 19,470 19,470 19,470
Mean 17.954 26.665 26.117 20.273 87.563 8.231 16.557 16.505 −0.776 −0.975
SD 2.845 1.426 1.512 1.925 8.37 1.11 1.521 1.664 0.766 0.9
Min. 0 22.816 22.69 13.714 58.805 4.088 12.547 12.547 −4.094 −4.094
Max. 25.042 30.087 30.087 25.292 109.471 9.883 19.55 20.926 0 0
Inter. goods
No. of observations 19,470 19,470 19,470 19,470 19,470 1,770 19,470 19,470 19,470 19,470
Mean 18.115 26.665 26.117 20.101 86.547 8.231 16.557 16.505 −0.776 −0.975
SD 2.857 1.426 1.512 1.946 8.306 1.11 1.521 1.664 0.766 0.9
Min. 0 22.816 22.69 14.745 63.195 4.088 12.547 12.547 −4.094 −4.094
Max. 25.535 30.087 30.087 24.466 105.083 9.883 19.55 20.926 0 0
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4.4
|
Robustness tests
Table 6 presents the results of the robustness tests for the regression results reported in Table 5.
The interaction terms between lnCand lnpercapitaGDP of an importing country are included in
the specification to ensure that the interaction terms between lnCand lnIPEQI are not driven by
the income factor. The first column for each sector in Table 6 shows the regression result of Equa-
tion (13) extended by the interaction term between lnCand lnpercapitaGDP. The coefficients of
these additional variables are significantly positive at the 1% level, but their magnitudes are rela-
tively trivial. The regression results for the variables of interest are consistent with the benchmark
model.
The population variables of the exporting and importing countries are incorporated into the
specification to account for the factor endowment characteristics, in line with the Hecksher–Ohlin
and non‐homothetic taste factors addressed by Bergstrand (1989), given that the lnGDPs and lnper-
capitaGDPs of exporting and importing countries are equivalent to their lnGDPs and lnPOPs in
the specification. The second column for each sector in Table 6 shows the regression result of
Equation (13) extended by population variables. The signs of the coefficients of these additional
variables are consistent with those in the literature. More importantly, the signs of the coefficients
TABLE 5 Regression results (Hausman–Taylor estimation)
(1) Consumption goods
(2) Intermediate goods
Dependent variable: lnX
eit
lnGDP
et
3.342*** 0.950***
(22.73) (6.55)
lnGDP
it
1.940*** 1.212***
(20.12) (11.57)
lnC
it
−0.812*** −1.297***
(−2.99) (−4.45)
lnC*lnIPEQI
it
0.167*** 0.356***
(2.87) (5.77)
lnDIS
ei
−1.423*** −1.591***
(−31.12) (−37.86)
lnCT
ei
−0.119 −0.152
(−0.83) (−1.15)
lnCL
ei
0.419*** 0.301***
(3.62) (2.83)
lnCO
ei
0.355** 0.657***
(2.40) (4.83)
No. of observations 19,470 19,470
χ
2
(111) 18,731.08*** 17,034.05***
Over‐identification test: χ
2
(19) 0.00 0.00
Notes: The figures in parentheses are z‐values. The values for the MRC, MRCE, MRDIS, MRCT, MRCL, MRCO, exporter
dummy, importer dummy, year dummy and the constant do not appear in the table, although they are included in the analysis.
*Significance at the 10% level, **Significance at the 5% level, ***Significance at the 1% level.
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TABLE 6 Regression results (Hausman–Taylor estimation)
Consumption goods
Intermediate goods
Dependent variable: lnX
eit
(1) (2) (3) (1) (2) (3)
lnGDP
et
3.295*** 3.474*** 3.626*** 0.945*** 1.013*** 1.327***
(22.42) (23.62) (23.56) (6.53) (6.96) (8.73)
lnGDP
it
1.222*** 2.011*** 2.118*** 0.884*** 1.309*** 1.473***
(8.94) (20.86) (21.27) (5.75) (12.26) (13.43)
lnC
it
−1.254*** −0.769*** −0.745*** −1.462*** −1.242*** −1.309***
(−4.53) (−2.85) (−2.76) (−4.92) (−4.26) (−4.50)
lnC*lnIPEQI
it
0.148** 0.166*** 0.155*** 0.334*** 0.342*** 0.345***
(2.55) (2.88) (2.68) (5.38) (5.54) (5.60)
lnC*
lnpercapitaGDP
it
0.055*** ––0.025*** ––
(7.41) (2.90)
lnDIS
ei
−1.423*** −1.423*** −1.423*** −1.591*** −1.591*** 1.591***
(−31.12) (−31.12) (−31.11) (−37.86) (−37.86) (37.86)
lnCT
ei
−0.119 −0.119 −0.119 −0.152 −0.152 −0.152
(−0.83) (−0.83) (−0.83) (−1.15) (−1.15) (−1.15)
lnCL
ei
0.419*** 0.419*** 0.419*** 0.301*** 0.301*** 0.301***
(3.62) (3.62) (3.62) (2.83) (2.83) (2.83)
lnCO
ei
0.355** 0.355** 0.355** 0.657*** 0.657*** 0.657***
(2.40) (2.40) (2.40) (4.83) (4.83) (4.83)
lnPOP
et
–−3.508*** −3.712*** –−1.723*** −2.145***
(−11.30) (−11.74) (−5.61) (−6.87)
lnPOP
it
–−1.767*** −1.844*** –−1.027*** −1.140***
(−9.19) (−9.54) (−5.23) (−5.78)
(Continues)
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TABLE 6 (Continued)
Consumption goods
Intermediate goods
Dependent variable: lnX
eit
(1) (2) (3) (1) (2) (3)
lnSER
et
––−0.070*** ––−0.143***
(−3.39) (−7.08)
lnSER
it
––−0.072*** ––−0.099***
(−3.90) (−5.44)
No. of observations 19,470 19,470 19,470 19,470 19,470 19,470
χ
2
(112, 113, 115) 18,809.19*** 19,022.26*** 19,058.52*** 17,043.20*** 17,099.65*** 17,191.16***
Over‐identification test:
χ
2
(20, 21, 23)
0.00 0.00 0.00 0.00 0.00 0.00
Notes: The figures in parentheses are z‐values. The values for the MRC, MRCE, MRDIS, MRCT, MRCL, MRCO, exporter dummy, importer dummy, year dummy and the constant do not appear
in the table, although they are included in the analysis.
*Significance at the 10% level, **Significance at the 5% level, ***Significance at the 1% level.
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of the variables of interest are equal to those of the benchmark model and the magnitudes of the
coefficients change only slightly.
To disentangle the pure effect of importers’preferences for environmental quality on the
export competition between China and OECD countries in third markets, an attempt has been
made to control for the effect of environmental regulations on bilateral trade in the specifica-
tion. To achieve this, the narrow measure regarding the strictness of environmental regulations
adopted by Van Beers and van den Bergh (1997) is incorporated into Equation (13).
16
Van
Beers and van den Bergh (1997) found that the estimated coefficients of the measures for both
exporters and importers were statistically significant and negative, although those for importers
are theoretically positive. The third column for each sector in Table 6 presents the regression
results of Equation (13) extended by the measures of the strictness of environmental regula-
tions. Following the approach of Van Beers and van den Bergh (1997), the population vari-
ables of the exporting and importing countries are incorporated into the extended specification
together. The coefficients on the measures of the strictness of environmental regulations are
consistent with those of Van Beers and van den Bergh (1997). In addition, the regression
results for the variables of interest almost follow those of the benchmark model. In sum, it can
be concluded that the main regression results of this study are indeed robust across various
specifications.
5
|
CONCLUSION
This study makes several significant contributions to the literature. First, in examining the
export competition between China and OECD countries, it assumes that importers’preferences
for environmentally friendly products are heterogeneous among countries. Second, a new
measure is proposed to represent importers’revealed preferences for environmental quality
across countries. Third, the theoretical gravity model is used to systemically investigate the
effect of China’s export growth on the exports of OECD countries in third markets.
OECD countries can regard China’s export growth as both a threat and an opportunity. As
the results of this study indicate, the threat is manifested in direct crowding out in markets for
consumption goods and partial dampening in markets for intermediate goods. The crowding‐out
and dampening effects of China’s export growth on the exports of OECD countries present
significant obstacles to achieving export equality. However, a good way to reduce the export
inequality arising from the threat posed by China is to deal with the variation in importers’
preferences for environmental quality, in that the crowding‐out and dampening effects are
weaker in export destinations where importers have greater preferences for environmental qual-
ity. This finding is confirmed by the panel data of observations for the 30 OECD exporting
countries and the 60 importing countries over the 2000–10 period. Providing that China is
slower in catching up with OECD exporters, the inequality of export growth is expected to
narrow as the export markets in which consumers have strong preferences for environmental
quality expand.
16
As the country coverage of this study is broader than that of Van Beers and van den Bergh (1997), energy use (kg of oil
equivalent) per US$1,000 GDP (constant 2005 PPP) obtained from the World Development Indicator data set is used to
derive the measure of the strictness of environmental regulations. In addition, the base year for the change of energy inten-
sity is 1995 instead of 1980 due to data limitations.
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ORCID
Jung Joo La http://orcid.org/0000-0003-1507-8698
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How to cite this article: La, J. J. Effects of the preference for environmental quality on the
export competition between China and OECD countries. World Econ. 2018;00:1–20.
https://doi.org/10.1111/twec.12732
APPENDIX A
TABLE A1 Country coverage
Exporters (30) Importers (60)
Australia Argentina Latvia
Austria Australia Lebanon
Belgium Austria Lithuania
Canada Belarus Luxembourg
Czech Rep. Belgium Malaysia
Denmark Brazil Malta
Finland Bulgaria Mexico
France Canada Netherlands
Germany Chile New Zealand
Greece Hong Kong Norway
Hungary Colombia Oman
Iceland Croatia Philippines
Ireland Cyprus Poland
Italy Czech Rep. Portugal
Japan Denmark South Korea
Luxembourg Ecuador Romania
(Continues)
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TABLE A1 (Continued)
Exporters (30) Importers (60)
Mexico Estonia Russian Federation
Netherlands Finland Saudi Arabia
New Zealand France Singapore
Norway Germany Slovakia
Poland Greece Slovenia
Portugal Hungary South Africa
South Korea Iceland Spain
Slovakia India Sweden
Spain Indonesia Switzerland
Sweden Ireland Thailand
Switzerland Israel Turkey
Turkey Italy UK
UK Japan USA
USA Jordan Vietnam
APPENDIX B
REGRESSION RESULTS FOR CAPITAL GOODS
For capital goods, the coefficient of lnCis significantly positive at the 5% level, whereas that of the
interaction term between lnCand lnIPEQI is significantly negative at the 1% level. More specifically,
the negative slope of the lnCcurve becomes steeper as the value of lnIPEQI increases, with −0.304,
−0.315, −0.318, −0.320 and −0.330 corresponding to the minimum (4.239), 1st quartile (4.289), 2nd
quartile (4.302), 3rd quartile (4.311) and 4th quartile (4.355) of the lnIPEQI, respectively. This result
confirms the expectation in Section 2 that the crowding‐out effect of China’s export growth on the
exports of OECD countries is the most severe in this sector. However, as capital goods are included
in the non‐environmental demand sector, the variable for lnIPEQI cannot be regarded as reflecting an
importer’s preference for environmental quality, as explained in Section 3. Rather, this value repre-
sents the level of the importing country’s income, given that the correlation coefficient between the
logarithm of the average IPEQI for capital goods and the logarithm of the average income from 2000
to 2010 through 99 observations is 0.67 at the 1% significance level. Thus, it is expected that import-
ing countries with higher incomes prefer exports from China to those from OECD countries because
these countries are willing to obtain cheaper capital goods to cover their higher labour costs. Further-
more, it is predicted that richer countries are better able to use China’s capital goods as a result of
their better production technology and infrastructure to produce high‐quality consumption goods or
intermediate goods. The positive relationship between income and production process sophistication
supports this argument. The correlation coefficient between the average income and the average level
of production process sophistication from 2007 to 2010 through 118 observations is 0.8035 at the 1%
significance level.
17
17
The global competitiveness index published by World Economic Forum offers the production process sophistication index
measuring to what extent firms in a country use the latest technologies for production.
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